Co-expression network analysis for identification of novel biomarkers of bronchopulmonary dysplasia model

BACKGROUND: Bronchopulmonary dysplasia (BPD) is the most common neonatal chronic lung disease. However, its exact molecular pathogenesis is not understood. We aimed to identify relevant gene modules that may play crucial roles in the occurrence and development of BPD by weighted gene co-expression network analysis (WGCNA). METHODS: We used RNA-Seq data of BPD and healthy control rats from our previous studies, wherein data from 30 samples was collected at days 1, 3, 7, 10, and 14. Data for preprocessing analysis included 17,613 differentially expressed genes (DEGs) with false discovery rate <0.05. RESULTS: We grouped the highly correlated genes into 13 modules, and constructed a network of mRNA gene associations, including the 150 most associated mRNA genes in each module. Lgals8, Srpra, Prtfdc1, and Thap11 were identified as the key hub genes. Enrichment analyses revealed Golgi vesicle transport, coated vesicle, actin-dependent ATPase activity and endoplasmic reticulum pathways associated with these genes involved in the pathological process of BPD in module. CONCLUSIONS: This is a study to analyze data obtained from BPD animal model at different time-points using WGCNA, to elucidate BPD-related susceptibility modules and disease-related genes.